Hluboké učení pro autonomní off-road řízení v simulaci

Type of document

Author

Supervisor

Zimmermann Karel

Opponent

Ecorchard Gaël Pierre Marie

Field of study

Kybernetika a robotika

Study program

Kybernetika a robotika

Institutions assigning rank

katedra řídicí techniky

Rights

A university thesis is a work protected by the Copyright Act. Extracts, copies and transcripts of the thesis are allowed for personal use only and at one?s own expense. The use of thesis should be in compliance with the Copyright Act http://www.mkcr.cz/assets/autorske-pravo/01-3982006.pdf and the citation ethics http://knihovny.cvut.cz/vychova/vskp.html.Vysokoškolská závěrečná práce je dílo chráněné autorským zákonem. Je možné pořizovat z něj na své náklady a pro svoji osobní potřebu výpisy, opisy a rozmnoženiny. Jeho využití musí být v souladu s autorským zákonem http://www.mkcr.cz/assets/autorske-pravo/01-3982006.pdf a citační etikou http://knihovny.cvut.cz/vychova/vskp.html.

Metadata

Abstract

This thesis presents different ways to make a car autonomous. We will use the power of machine learning and neural network to ?teach? a car how to drive autonomously in an off-road environment by using only a minimum set of sensors, in our case which is just a single RGB camera. We will first focus on a technique called imitation learning, it is a supervised learning algorithm which takes a lot of example pairs (image; driving command) to extract a policy that the car will use to drive in unseen situations. Then we will use the so-called reinforcement learning technique. It is an unsupervised learning algorithm which manages, by a lot of trial and error experiments, to create a policy used by the car to drive safely. We managed with these two techniques to make our car drive itself in a simulator.

This thesis presents different ways to make a car autonomous. We will use the power of machine learning and neural network to ?teach? a car how to drive autonomously in an off-road environment by using only a minimum set of sensors, in our case which is just a single RGB camera. We will first focus on a technique called imitation learning, it is a supervised learning algorithm which takes a lot of example pairs (image; driving command) to extract a policy that the car will use to drive in unseen situations. Then we will use the so-called reinforcement learning technique. It is an unsupervised learning algorithm which manages, by a lot of trial and error experiments, to create a policy used by the car to drive safely. We managed with these two techniques to make our car drive itself in a simulator.